Hi everyone,
This month’s meeting of the Machine Learning for Climate and Weather Working Group will take place this Friday, 7th November, at 2pm (AEDT), and includes a talk from Ajitha Cyriac (CSIRO) - A machine learning approach to downscale sea surface temperature extremes and thermal stress on the Great Barrier Reef
NEW ZOOM DETAILS (for this month’s meeting only): https://anu.zoom.us/j/81754231561?pwd=b7cQMUUdqvTcXeYZ8acztclzavn8Rc.1
Meeting ID: 817 5423 1561, Password: 292009
Agenda:
Facilitator: Ryan Holmes
Co-chairs: Tennessee Leeuwenburg, Sanaa Hobeichi, Vassili Kitsios, Micael Oliveira (ACCESS-NRI liaison), and Yue Sun (NCI liaison)
- Acknowledgement of country
- Updates from ACCESS-NRI and NCI
- Feedback collected from the audience of the NCI Deep Learning Model Development in Climate and Weather Studies Webinar (30 Oct) and Exercise session (6 Nov).
- NCI dk92 software environment and example notebook update
- Updates from the Chairs
- Momentum Partnership ML Hackathon - 10-14 November
- Updates from the Community & new community member introductions
- Presentation by Ajitha Cyriac (CSIRO) (Details provided below).
- Discussion (ongoing, dependent on time available in meeting):
- Plans for using project nm47 (100TB gdata storage, 875kSU/quarter) on NCI.
- Completed projects:
- Sam Green - brief overview of https://forum.access-hive.org.au/t/experiment-proposal-processing-global-km-scale-hackathon-data/5347
- Proposed projects:
- Taimoor’s proposal for support: https://forum.access-hive.org.au/t/applying-to-work-with-access-nri-on-ml-applications-to-earth-systems/5366/5
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Presenter: Ajitha Cyraic | Postdoctoral Research Fellow at CSIRO
Title: A machine learning approach to downscale sea surface temperature extremes and thermal stress on the Great Barrier Reef
Abstract: In this study, we have successfully developed a machine learning (ML) model based on a super resolution deconvolutional neural network to rapidly downscale SST on the Great Barrier Reef (GBR). When downscaling 80 km data to 10 km resolution, the ML model captures the spatial variability of SST and extreme thermal events significantly better than a conventional interpolation method. We have applied this model to independent datasets from both current and future climates to demonstrate its robustness. We further use this model to downscale thermal stress from five CMIP6 models and analyse coral bleaching risk under different emission scenarios on the GBR.
Biography: Ajitha Cyriac is a Postdoctoral Research Fellow at CSIRO based in Perth. Her research focuses on downscaling climate data using ML methods and analyzing coral bleaching risks at Australia’s Marine World Heritage Areas.